Biology subjects

Minot, S.

Publications and source records attributed to Minot, S..

5 recordsLinked to original sources

bioRxiv2026

Dysregulation of epithelial differentiation architecture underlies field cancerization in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) has exceptionally high recurrence rates, exceeding 50%, contributing to mortality and morbidity. This recurrence rate is proposed to be due to field cancerization, a phenomenon where tumor-adjacent "field" tissue that appears histologically normal harbors molecular changes that prime it for subsequent tumor growth. Current models attribute field cancerization to shared driver mutations between tumors and adjacent at-risk tissue, but this assumption has never been directly tested. To comprehensively characterize this phenomenon, we profiled patient-matched tumor, normal-adjacent field (NAF), contralateral normal, and blood samples from HPV-negative HNSCC patients using whole genome sequencing, targeted driver gene sequencing, single-nucleus multiome profiling, spatial transcriptomics and functional proliferation assays. While histologically normal NAF tissues did harbor expanded mutant clones, these mutations rarely overlapped with paired tumors and were not shared across patients, suggesting molecular features beyond mutations are altered in NAF. Single-cell multiomics revealed that NAF epithelium loses its normal organizational hierarchy: basal stem cell markers persist in differentiated cells, while terminal differentiation programs fail to activate, with reconfiguration of both gene regulatory networks and cell-cell interactions. Proliferating cells appeared in tissue layers normally restricted to post-mitotic cells, a pattern typically seen only in overt dysplasia. Within this disrupted hierarchy, a distinct epithelial state arose in NAF that expressed both normal epithelial and tumor-associated markers, which we localized to the parabasal layers using immunofluorescence and spatial transcriptomics. Together, our findings demonstrate that field cancerization operates through epithelial hierarchy disorganization that disrupts differentiation, generates cells with tumor-like features, and may create a permissive environment for the expansion of cancer-associated mutations, all within tissue that appears histologically normal.

cancer biology↗

bioRxiv2025

TCR2HLA: calibrated inference of HLA genotypes from TCR repertoires enables identification of immunologically relevant metaclonotypes

T cell receptors (TCRs) recognize peptides presented by polymorphic human leukocyte antigen (HLA) molecules, but HLA genotype data are often missing from TCR repertoire sequencing studies. To address this, we developed TCR2HLA, an open-source tool that infers HLA genotypes from TCR{beta} repertoires. Expanding on work linking public TRBV-CDR3 sequences to HLA genotypes, we incorporated "quasi-public" metaclonotypes - composed of rarer TCR{beta} sequences with shared amino acid features - enriched by HLA genotypes. Using four TCR{beta}seq datasets from 3,150 individuals, we applied TRBV gene partitioning and locality-sensitive hashing to identify [~]96,000 TCR{beta} features strongly associated with specific HLA alleles from 71M input TCRs. Binary HLA classifiers built with these features achieved high balanced accuracy (>0.9) across common HLA-A (9/12), B (9/12), C (6/13), DRB1 (11/11) alleles and prevalent DPA1/DPB1 (6/10), DQA1/DQB1 (8/17) heterodimers. We also introduced a high-sensitivity calibration to support predictions in samples with as few as 5,000 unique clonotypes. Calibrated predictions with confidence filtering improved reliability. Beyond genotype imputation, TCR2HLA enables the discovery of novel HLA- and exposure-associated TCRs, as shown by the identification of SARS-CoV-2 related TCRs in a large COVID-19 dataset lacking HLA data. TCR2HLA provides a scalable framework for bridging the gap between TCRseq data and HLA genotype for biomarker discovery.

immunology↗

bioRxiv2023

The Regulation of Nucleic Acid Vaccine Responses by the Microbiome

SummaryNucleic acid vaccines, including both RNA and DNA platforms, are key technologies that have considerable promise in combating both infectious disease and cancer. However, little is known about the extrinsic factors that regulate nucleic acid vaccine responses and which may determine their effectiveness. The microbiome is recognized as a significant regulator of immune development and response, whose role in regulating some traditional vaccine platforms has recently been discovered. Using germ-free and specific-pathogen-free mouse models in combination with different protein, DNA, and mRNA vaccine regimens, we demonstrate that the microbiome is a significant regulator of nucleic acid vaccine immunogenicity. While the presence of the microbiome enhances CD8+ T cell responses to mRNA lipid nanoparticle (LNP) immunization, the microbiome suppresses immunoglobulin and CD4+ T cell responses to DNA-prime, DNA-protein-boost immunization, indicating contrasting roles for the microbiome in the regulation of these different nucleic acid vaccine platforms. In the case of mRNA-LNP vaccination, germ-free mice display reduced dendritic cell/macrophage activation that may underlie the deficient vaccine response. Our study identifies the microbiome as a relevant determinant of nucleic acid vaccine response with implications for their continued therapeutic development and deployment.

immunology↗

bioRxiv2021

Genomic Stability and Genetic Defense Systems in Dolosigranulum pigrum a Candidate Beneficial Bacterium from the Human Microbiome

Dolosigranulum pigrum is positively associated with indicators of health in multiple epidemiological studies of human nasal microbiota. Knowledge of the basic biology of D. pigrum is a prerequisite for evaluating its potential for future therapeutic use; however, such data are very limited. To gain insight into D. pigrums chromosomal structure, pangenome and genomic stability, we compared the genomes of 28 D. pigrum strains that were collected across 20 years. Phylogenomic analysis showed closely related strains circulating over this period and closure of 19 genomes revealed highly conserved chromosomal synteny. Gene clusters involved in the mobilome and in defense against mobile genetic elements (MGEs) were enriched in the accessory genome versus the core genome. A systematic analysis for MGEs identified the first candidate D. pigrum prophage and insertion sequence. A systematic analysis for genetic elements that limit the spread of MGEs, including restriction modification (RM), CRISPR-Cas, and deity-named defense systems, revealed strain-level diversity in host defense systems that localized to specific genomic sites including one RM system hotspot. Analysis of CRISPR spacers pointed to a wealth of MGEs against which D. pigrum defends itself. These results reveal a role for horizontal gene transfer and mobile genetic elements in strain diversification while highlighting that in D. pigrum this occurs within the context of a highly stable chromosomal organization protected by a variety of defense mechanisms. IMPORTANCEDolosigranulum pigrum is a candidate beneficial bacterium with potential for future therapeutic use. This is based on its positive associations with characteristics of health in multiple studies of human nasal microbiota across the span of human life. For example, high levels of D. pigrum nasal colonization in adults predicts the absence of Staphylococcus aureus nasal colonization. Also, D. pigrum nasal colonization in young children is associated with healthy control groups in studies of middle ear infections. Our analysis of 28 genomes revealed a remarkable stability of D. pigrum strains colonizing people in the U.S. across a 20-year span. We subsequently identified factors that can influence this stability, including genomic stability, phage predators, the role of MGEs in strain-level variation and defenses against MGEs. Finally, these D. pigrum strains also lacked predicted virulence factors. Overall, these findings add additional support to the potential for D. pigrum as a therapeutic bacterium.

microbiology↗

bioRxiv2017

Comprehensive Benchmarking and Ensemble Approaches for Metagenomic Classifiers

BackgroundOne of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest (n=35) to date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of eleven metagenomics classifiers. We also assess the effects of filtering and combining tools to reduce the number of false positives.\n\nResultsTools were characterized on the basis of their ability to (1) identify taxa at the genus, species, and strain levels, (2) quantify relative abundance measures of taxa, and (3) classify individual reads to the species level. Strikingly, the number of species identified by the eleven tools can differ by over three orders of magnitude on the same datasets. However, various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Indeed, leveraging tools with different heuristics is beneficial for improved precision. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species and where customized tools may be required.\n\nConclusionsThe results of this study provide positive controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision and recall. We show that proper experimental design and analysis parameters, including depth of sequencing, choice of classifier or classifiers, database size, and filtering, can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

genomics↗